OpenRaiser/Intern-Atlas huggingface dataset:
Energy-efficient Alternating Iterative Secure Structure of Maximizing Secrecy Rate for Directional Modulation Networks
Venue: arXiv cs.IT 2019
Year: 2019

In a directional modulation (DM) network, the issues of security and privacy have taken on an increasingly important role. Since the power allocation of confidential message and artificial noise will make a constructive effect on the system performance, it is important to jointly consider the relationship between the beamforming vectors and the power allocation (PA) factors. To maximize the secrecy rate (SR), an alternating iterative structure (AIS) between the beamforming and PA is proposed. With only two or three iterations, it can rapidly converge to its rate ceil. Simulation results indicate that the SR performance of proposed AIS is much better than the null-space projection (NSP) based PA strategy in the medium and large signal-to-noise ratio (SNR) regions, especially when the number of antennas at the DM transmitter is small.
The Impact of Tribalism on Social Welfare
Venue: arXiv cs.GT 2019
Year: 2019

We explore the impact of mutual altruism among the players belonging to the same set -- their tribe -- in a partition of all players in arbitrary strategic games upon the quality of equilibria attained. To this end, we introduce the notion of a τ-tribal extension of an arbitrary strategic game, in which players' subjective cost functions are updated to reflect this, and the associated Price of Tribalism, which is the ratio of the social welfare of the worst Nash equilibrium of the tribal extension to that of the optimum of social welfare. We show that in a well-known game of friendship cliques, network contribution games as well as atomic linear congestion games, the Price of Tribalism is higher than the Price of Anarchy of either the purely selfish players or fully altruistic players (i.e. ones who seek to maximise the social welfare). This phenomenon is observed under a variety of equilibrium concepts. In each instance, we present upper bounds on the Price of Tribalism that match the lower bounds established by our example.
Cascade RetinaNet: Maintaining Consistency for Single-Stage Object Detection
Venue: arXiv cs.CV 2019
Year: 2019

Recent researches attempt to improve the detection performance by adopting the idea of cascade for single-stage detectors. In this paper, we analyze and discover that inconsistency is the major factor limiting the performance. The refined anchors are associated with the feature extracted from the previous location and the classifier is confused by misaligned classification and localization. Further, we point out two main designing rules for the cascade manner: improving consistency between classification confidence and localization performance, and maintaining feature consistency between different stages. A multistage object detector named Cas-RetinaNet, is then proposed for reducing the misalignments. It consists of sequential stages trained with increasing IoU thresholds for improving the correlation, and a novel Feature Consistency Module for mitigating the feature inconsistency. Experiments show that our proposed Cas-RetinaNet achieves stable performance gains across different models and input scales. Specifically, our method improves RetinaNet from 39.1 AP to 41.1 AP on the challenging MS COCO dataset without any bells or whistles.
A Unified Framework for Problems on Guessing, Source Coding and Task Partitioning
Venue: arXiv cs.IT 2019
Year: 2019

We study four problems namely, Campbell's source coding problem, Arikan's guessing problem, Huieihel et al.'s memoryless guessing problem, and Bunte and Lapidoth's task partitioning problem. We observe a close relationship among these problems. In all these problems, the objective is to minimize moments of some functions of random variables, and Rényi entropy and Sundaresan's divergence arise as optimal solutions. This motivates us to establish a connection among these four problems. In this paper, we study a more general problem and show that Rényi and Shannon entropies arise as its solution. We show that the problems on source coding, guessing and task partitioning are particular instances of this general optimization problem, and derive the lower bounds using this framework. We also refine some known results and present new results for mismatched version of these problems using a unified approach. We strongly feel that this generalization would, in addition to help in understanding the similarities and distinctiveness of these problems, also help to solve any new problem that falls in this framework.
Partial Solvers for Generalized Parity Games
Venue: arXiv cs.GT 2019
Year: 2019

Parity games have been broadly studied in recent years for their applications to controller synthesis and verification. In practice, partial solvers for parity games that execute in polynomial time, while incomplete, can solve most games in publicly available benchmark suites. In this paper, we combine those partial solvers with the classical recursive algorithm for parity games due to Zielonka. We also extend partial solvers to generalized parity games that are games with conjunction of parity objectives. We have implemented those algorithms and evaluated them on a large set of benchmarks proposed in the last LTL synthesis competition.
Human Pose Estimation for Real-World Crowded Scenarios
Venue: arXiv cs.CV 2019
Year: 2019

Human pose estimation has recently made significant progress with the adoption of deep convolutional neural networks. Its many applications have attracted tremendous interest in recent years. However, many practical applications require pose estimation for human crowds, which still is a rarely addressed problem. In this work, we explore methods to optimize pose estimation for human crowds, focusing on challenges introduced with dense crowds, such as occlusions, people in close proximity to each other, and partial visibility of people. In order to address these challenges, we evaluate three aspects of a pose detection approach: i) a data augmentation method to introduce robustness to occlusions, ii) the explicit detection of occluded body parts, and iii) the use of the synthetic generated datasets. The first approach to improve the accuracy in crowded scenarios is to generate occlusions at training time using person and object cutouts from the object recognition dataset COCO (Common Objects in Context). Furthermore, the synthetically generated dataset JTA (Joint Track Auto) is evaluated for the use in real-world crowd applications. In order to overcome the transfer gap of JTA originating from a low pose variety and less dense crowds, an extension dataset is created to ease the use for real-world applications. Additionally, the occlusion flags provided with JTA are utilized to train a model, which explicitly distinguishes between occluded and visible body parts in two distinct branches. The combination of the proposed additions to the baseline method help to improve the overall accuracy by 4.7% AP and thereby provide comparable results to current state-of-the-art approaches on the respective dataset.
A Quadrotor with an Origami-Inspired Protective Mechanism
Venue: arXiv cs.RO 2019
Year: 2019

Despite advances in localization and navigation, aerial robots inevitably remain susceptible to accidents and collisions. In this work, we propose a passive foldable airframe as a protective mechanism for a small aerial robot. A foldable quadrotor is designed and fabricated using the origami-inspired manufacturing paradigm. Upon an accidental mid-flight collision, the deformable airframe is mechanically activated. The rigid frame reconfigures its structure to protect the central part of the robot that houses sensitive components from a crash to the ground. The proposed robot is fabricated, modeled, and characterized. The 51-gram vehicle demonstrates the desired folding sequence in less than 0.15 s when colliding with a wall when flying.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data
Venue: arXiv cs.CV 2019
Year: 2019

In recent years, deep learning models have resulted in a huge amount of progress in various areas, including computer vision. By nature, the supervised training of deep models requires a large amount of data to be available. This ideal case is usually not tractable as the data annotation is a tremendously exhausting and costly task to perform. An alternative is to use synthetic data. In this paper, we take a comprehensive look into the effects of replacing real data with synthetic data. We further analyze the effects of having a limited amount of real data. We use multiple synthetic and real datasets along with a simulation tool to create large amounts of cheaply annotated synthetic data. We analyze the domain similarity of each of these datasets. We provide insights about designing a methodological procedure for training deep networks using these datasets.
Massive MU-MIMO-OFDM Uplink with Direct RF-Sampling and 1-Bit ADCs
Venue: arXiv cs.IT 2019
Year: 2019

Advances in analog-to-digital converter (ADC) technology have opened up the possibility to directly digitize wideband radio frequency (RF) signals, avoiding the need for analog down-conversion. In this work, we consider an orthogonal frequency-division multiplexing (OFDM)-based massive multi-user (MU) multiple-input multiple-output (MIMO) uplink system that relies on direct RF-sampling at the base station and digitizes the received RF signals with 1-bit ADCs. Using Bussgang's theorem, we provide an analytical expression for the error-vector magnitude (EVM) achieved by digital down-conversion and zero-forcing combining. Our results demonstrate that direct RF-sampling 1-bit ADCs enables low EVM and supports high-order constellations in the massive MU-MIMO-OFDM uplink.
Measuring I2P Censorship at a Global Scale
Venue: arXiv cs.CY 2019
Year: 2019

The prevalence of Internet censorship has prompted the creation of several measurement platforms for monitoring filtering activities. An important challenge faced by these platforms revolves around the trade-off between depth of measurement and breadth of coverage. In this paper, we present an opportunistic censorship measurement infrastructure built on top of a network of distributed VPN servers run by volunteers, which we used to measure the extent to which the I2P anonymity network is blocked around the world. This infrastructure provides us with not only numerous and geographically diverse vantage points, but also the ability to conduct in-depth measurements across all levels of the network stack. Using this infrastructure, we measured at a global scale the availability of four different I2P services: the official homepage, its mirror site, reseed servers, and active relays in the network. Within a period of one month, we conducted a total of 54K measurements from 1.7K network locations in 164 countries. With different techniques for detecting domain name blocking, network packet injection, and block pages, we discovered I2P censorship in five countries: China, Iran, Oman, Qatar, and Kuwait. Finally, we conclude by discussing potential approaches to circumvent censorship on I2P.
Hydrodynamic Simulations using GPGPU Architectures
Venue: arXiv cs.CE 2019
Year: 2019

Simulating the flow of different fluids can be a highly computational intensive process, which requires large amounts of resources. Recently there has been a lot of research effort directed towards GPU processing, which can greatly increase the performance of different applications, such as Smoothed Particle Hydrodynamics (SPH), which is most commonly used for hydrodynamic simulations. Smoothed particle hydrodynamics (SPH) is a numerical method commonly used in Computational Fluid Dynamics (CFD). It is a method that can simulate particle flow and interaction with structures and highly deformable bodies. It replaces the fluid with a set of particles that carry properties such as mass, speed and position that move according to the governing dynamics. The dynamics of fluids are based on the Navier-Stokes equations. These describe the physical properties of continuous fields in the fluid. SPH approximates these equations using an integral interpolant that is then solved numerically. This article addresses the current state of technologies available that can be used to speed up the algorithm and proposes a set of optimizations that can be achieved by using different frameworks. We also draw conclusions regarding the equilibrium between performance and accuracy, using different numerical algorithms, frameworks and hardware optimizations.
The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost
Venue: arXiv cs.LG 2019
Year: 2019

Privacy has raised considerable concerns recently, especially with the advent of information explosion and numerous data mining techniques to explore the information inside large volumes of data. In this context, a new distributed learning paradigm termed federated learning becomes prominent recently to tackle the privacy issues in distributed learning, where only learning models will be transmitted from the distributed nodes to servers without revealing users' own data and hence protecting the privacy of users. In this paper, we propose a horizontal federated XGBoost algorithm to solve the federated anomaly detection problem, where the anomaly detection aims to identify abnormalities from extremely unbalanced datasets and can be considered as a special classification problem. Our proposed federated XGBoost algorithm incorporates data aggregation and sparse federated update processes to balance the tradeoff between privacy and learning performance. In particular, we introduce the virtual data sample by aggregating a group of users' data together at a single distributed node. We compute parameters based on these virtual data samples in the local nodes and aggregate the learning model in the central server. In the learning model upgrading process, we focus more on the wrongly classified data before in the virtual sample and hence to generate sparse learning model parameters. By carefully controlling the size of these groups of samples, we can achieve a tradeoff between privacy and learning performance. Our experimental results show the effectiveness of our proposed scheme by comparing with existing state-of-the-arts.
ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining
Venue: arXiv cs.NE 2019
Year: 2019

The state-of-the-art approaches employ approximate computing to reduce the energy consumption of DNN hardware. Approximate DNNs then require extensive retraining afterwards to recover from the accuracy loss caused by the use of approximate operations. However, retraining of complex DNNs does not scale well. In this paper, we demonstrate that efficient approximations can be introduced into the computational path of DNN accelerators while retraining can completely be avoided. ALWANN provides highly optimized implementations of DNNs for custom low-power accelerators in which the number of computing units is lower than the number of DNN layers. First, a fully trained DNN is converted to operate with 8-bit weights and 8-bit multipliers in convolutional layers. A suitable approximate multiplier is then selected for each computing element from a library of approximate multipliers in such a way that (i) one approximate multiplier serves several layers, and (ii) the overall classification error and energy consumption are minimized. The optimizations including the multiplier selection problem are solved by means of a multiobjective optimization NSGA-II algorithm. In order to completely avoid the computationally expensive retraining of DNN, which is usually employed to improve the classification accuracy, we propose a simple weight updating scheme that compensates the inaccuracy introduced by employing approximate multipliers. The proposed approach is evaluated for two architectures of DNN accelerators with approximate multipliers from the open-source "EvoApprox" library. We report that the proposed approach saves 30% of energy needed for multiplication in convolutional layers of ResNet-50 while the accuracy is degraded by only 0.6%. The proposed technique and approximate layers are available as an open-source extension of TensorFlow at https://github.com/ehw-fit/tf-approximate.
The Complexity of Partial Function Extension for Coverage Functions
Venue: arXiv cs.DS 2019
Year: 2019

Coverage functions are an important subclass of submodular functions, finding applications in machine learning, game theory, social networks, and facility location. We study the complexity of partial function extension to coverage functions. That is, given a partial function consisting of a family of subsets of $[m]$ and a value at each point, does there exist a coverage function defined on all subsets of $[m]$ that extends this partial function? Partial function extension is previously studied for other function classes, including boolean functions and convex functions, and is useful in many fields, such as obtaining bounds on learning these function classes. We show that determining extendibility of a partial function to a coverage function is NP-complete, establishing in the process that there is a polynomial-sized certificate of extendibility. The hardness also gives us a lower bound for learning coverage functions. We then study two natural notions of approximate extension, to account for errors in the data set. The two notions correspond roughly to multiplicative point-wise approximation and additive $L_1$ approximation. We show upper and lower bounds for both notions of approximation. In the second case we obtain nearly tight bounds.
A Novel Slip-Kalman Filter to Track the Progression of Reading Through Eye-Gaze Measurements
Venue: arXiv cs.HC 2019
Year: 2019

In this paper, we propose an approach to track the progression of eye-gaze while reading a block of text on computer screen. The proposed approach will help to accurately quantify reading, e.g., identifying the lines of text that were read/skipped and estimating the time spent on each line, based on commercially available inexpensive eye-tracking devices. The proposed approach is based on a novel Slip Kalman filter that is custom designed to track the progression of reading. The performance of the proposed method is demonstrated using 25 pages eye-tracking data collected using a commercial desk-mounted eye-tracking device.
Style Transfer Applied to Face Liveness Detection with User-Centered Models
Venue: arXiv cs.CV 2019
Year: 2019

This paper proposes a face anti-spoofing user-centered model (FAS-UCM). The major difficulty, in this case, is obtaining fraudulent images from all users to train the models. To overcome this problem, the proposed method is divided in three main parts: generation of new spoof images, based on style transfer and spoof image representation models; training of a Convolutional Neural Network (CNN) for liveness detection; evaluation of the live and spoof testing images for each subject. The generalization of the CNN to perform style transfer has shown promising qualitative results. Preliminary results have shown that the proposed method is capable of distinguishing between live and spoof images on the SiW database, with an average classification error rate of 0.22.
Leveraging Linguistic Characteristics for Bipolar Disorder Recognition with Gender Differences
Venue: arXiv cs.IR 2019
Year: 2019

Most previous studies on automatic recognition model for bipolar disorder (BD) were based on both social media and linguistic features. The present study investigates the possibility of adopting only language-based features, namely the syntax and morpheme collocation. We also examine the effect of gender on the results considering gender has long been recognized as an important modulating factor for mental disorders, yet it received little attention in previous linguistic models. The present study collects Twitter posts 3 months prior to the self-disclosure by 349 BD users (231 female, 118 male). We construct a set of syntactic patterns in terms of the word usage based on graph pattern construction and pattern attention mechanism. The factors examined are gender differences, syntactic patterns, and bipolar recognition performance. The performance indicates our F1 scores reach over 91% and outperform several baselines, including those using TF-IDF, LIWC and pre-trained language models (ELMO and BERT). The contributions of the present study are: (1) The features are contextualized, domain-agnostic, and purely linguistic. (2) The performance of BD recognition is improved by gender-enriched linguistic pattern features, which are constructed with gender differences in language usage.
HODGEPODGE: Sound event detection based on ensemble of semi-supervised learning methods
Venue: arXiv cs.SD 2019
Year: 2019

In this paper, we present a method called HODGEPODGE\footnotemark[1] for large-scale detection of sound events using weakly labeled, synthetic, and unlabeled data proposed in the Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge Task 4: Sound event detection in domestic environments. To perform this task, we adopted the convolutional recurrent neural networks (CRNN) as our backbone network. In order to deal with a small amount of tagged data and a large amounts of unlabeled in-domain data, we aim to focus primarily on how to apply semi-supervise learning methods efficiently to make full use of limited data. Three semi-supervised learning principles have been used in our system, including: 1) Consistency regularization applies data augmentation; 2) MixUp regularizer requiring that the predictions for a interpolation of two inputs is close to the interpolation of the prediction for each individual input; 3) MixUp regularization applies to interpolation between data augmentations. We also tried an ensemble of various models, which are trained by using different semi-supervised learning principles. Our proposed approach significantly improved the performance of the baseline, achieving the event-based f-measure of 42.0\% compared to 25.8\% event-based f-measure of the baseline in the provided official evaluation dataset. Our submissions ranked third among 18 teams in the task 4.
putEMG -- a surface electromyography hand gesture recognition dataset
Venue: arXiv cs.HC 2019
Year: 2019

In this paper, we present a putEMG dataset intended for evaluation of hand gesture recognition methods based on sEMG signal. The dataset was acquired for 44 able-bodied subjects and include 8 gestures (3 full hand gestures, 4 pinches, and idle). It consists of uninterrupted recordings of 24 sEMG channels from the subject's forearm, RGB video stream and depth camera images used for hand motion tracking. Moreover, exemplary processing scripts are also published. putEMG dataset is available under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license at: https://www.biolab.put.poznan.pl/putemg-dataset/. The dataset was validated regarding sEMG amplitudes and gesture recognition performance. The classification was performed using state-of-the-art classifiers and feature sets. Accuracy of 90% was achieved for SVM classifier utilising RMS feature and for LDA classifier using Hudgin's and Du's feature sets. Analysis of performance for particular gestures showed that LDA/Du combination has significantly higher accuracy for full hand gestures, while SVM/RMS performs better for pinch gestures. Presented dataset can be used as a benchmark for various classification methods, evaluation of electrode localisation concepts, or development of classification methods invariant to user-specific features or electrode displacement.